Sign Language Digits

Sign Language Digits

Sign languages are languages that can include simultaneously employing hand gestures, movement, the orientation of the fingers, arms or body, and facial expressions to convey a speaker's ideas [1]. In this article, we develop a stacking classifier model from various scikit-learn classifiers for the Sign Language Digits dataset from Kaggle. The main purpose of this article is to demonstrate implementation of stacking classifiers.

Dataset

Investigating the data, we can see that y represent numbers 0 to 9 that encoded into dummy variables.

Train and Test sets

StratifiedKFold is a variation of k-fold which returns stratified folds: each set contains approximately the same percentage of samples of each target class as the complete set.

Modeling: Keras Multi-layer Perceptron (MLP) for Image Classifications

A multi-layer perceptron (MLP) is a class of feedforward artificial neural network (ANN). The algorithm at each iteration uses the Cross-Entropy Loss to measure the loss, and then the gradient and the model update is calculated. At the end of this iterative process, we would reach a better level of agreement between test and predicted sets since the error would be lower from that of the first step.

Setting up Tensor Arrays

Modeling

Fitting the model

Model Performance

Confusion Matrix

The confusion matrix allows for visualization of the performance of an algorithm. Note that due to the size of data, here we don't provide a Cross-validation evaluation. In general, this type of evaluation is preferred.


References

  1. Sign language Wikipedia Page
  2. Sign Language Digits Dataset
  3. Mower, Jeffrey P. "PREP-Mt: predictive RNA editor for plant mitochondrial genes." BMC bioinformatics 6.1 (2005): 1-15.
  4. Precision and recall wikipedia page

  5. Cross-validation: evaluating estimator performance